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Thomas Dearden & Petter Gottschalk (2020) Gender and White-Collar Crime:
Convenience in Target Selection, Deviant
Behavior, DOI: 10.1080/01639625.2020.1756428
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Gender and White-Collar Crime: Convenience in Target Selection
Thomas E Deardena* and Petter Gottschalkb
aDepartment of Sociology Virginia Tech 560 McBryde Hall 225 Stanger Street Blacksburg, VA 24061
USA +1-540-231-6074
tdearden@vt.edu
bDepartment of Leadership and Organizational Behavior BI Norwegian Business School
Nydalsveien 37 0484 Oslo
Norway +4746410716 petter.gottschalk@bi.no
Biographical Note:
Thomas Dearden is assistant professor of sociology at Virginia Tech. He graduated with B.A. in psychology from Brigham Young University – Hawaii and an M.A. and Ph.D. in criminology and criminal justice from Indiana University. Dr. Dearden specializes in research technology and crime, and corporate crime. He has conducted research for organizations across the globe, including the Polynesian Cultural Center in Hawaii, Food for Life Vrindavan in Uttar Pradesh, India, and Pay Tel in North Carolina. He has published his research in peer- reviewed journals including The Journal of Financial Crime and The Journal of Investigative Psychology and Offender Profiling and has presented at a dozen different conferences.
Petter Gottschalk is professor in the Department of Leadership and Organizational Behavior at BI Norwegian Business School in Oslo, Norway. After completing his education at
Technische Universität Berlin, Dartmouth College, Massachusetts Institute of Technology, and Henley Management College, he took on executive positions in technology enterprises for twenty years before joining academics. Dr. Gottschalk has published extensively on
knowledge management, intelligence strategy, police investigations, white-collar crime, convenience theory, and fraud examinations. He has lectured in the United States, China, Singapore, and Egypt.
3 Abstract
We find support for the idea that gender affects target selection when committing white-collar crime. Based on the theory of convenience, we argue that male and female offenders vary in their perceptions of convenience when considering alternative categories of crime and alternative categories of victims. We obtained data from Utah’s White Collar Crime Offender Registry. Individuals in the state of Utah who are convicted of a second-degree white-collar crime felony or higher are required to register. The categories of crime included from statute HB 378 are securities fraud, theft by deception, unlawful dealing of property by fiduciary, fraudulent insurance, mortgage fraud, communications fraud, and money laundering.
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Gender and White-Collar Crime: Convenience in Target Selection
White-collar crime is financial crime committed by individuals in privileged positions in business and public organizations (Logan, Morgan, Benson, and Cullen 2019; Sutherland 1983). This is a narrow definition expanded by researchers such as Galvin, Loughran, Simpson, and Cohen (2018), who includes all kinds of non-violent crime with financial motives. We apply in this research the wider definition of white-collar crime choosing the offense-based rather than the offender-based definition (Goossen, Seva, and Larsson 2016;
Piquero 2018; Piquero and Schoepfer 2010). An argument for the offense-based perspective is that everyone now can commit fraud on the Internet, an act that was impossible when
Sutherland (1939) coined the term white-collar crime eight decades ago (Geest, Weisburd, and Blokland 2017:544):
In sharp contrast to the 1940s however, when most financial crimes were out of reach for ordinary people, in modern-day society the opportunity structure for white-collar crime has dramatically changed. The growth of the credit economy, the increase of the service sector, increased urbanization, and the advent of the internet – to name but a few factors – have increasingly democratized the phenomenon of financial crimes and fraud. With the advancement of technology, crimes labelled as ‘white-collar’ do not require employment or specific skills, and have increasingly come within range of the poor and disadvantaged who disproportionately came in contact with the criminal justice system then and now.
Research has emphasized that female and male offenders do not have the same
motives, opportunities, and willingness to commit white-collar crime (Benson and Harbinson 2020; Galvin 2020; Goulette 2020; Harbinson 2020; Ndrecka 2020). This article focuses on a slightly different perspective by addressing the following research question: How does gender affect target selection in white-collar crime? Based on the theory of convenience (Gottschalk
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2017, 2020), this article explores both theoretically and empirically how variation in convenience between men and women might influence the implicit choice of victim when committing white-collar crime.
Nature of White Collar Crime
White-collar crime includes all kinds of financial offences, such as fraud, manipulation, and corruption (Dearden 2016, 2017, 2019). The theory of convenience
suggests that the relative convenience of alternative actions determines decision-making when committing white-collar crime (Braaten and Vaughn 2019; Chan and Gibbs 2020; Gottschalk 2017, 2020; Hansen 2020; Kireenko, Nevzorova, and Fedotov 2019; Vasiu and Podgor 2019).
It is a crime-as-choice theory, where individuals make the choice of crime when they perceive crime as more convenient than alternative actions. Convenience is the state of being able to proceed with something with little effort or difficulty, avoiding pain and strain (Mai and Olsen 2016). Convenience is savings in time and effort (Farquhar and Rowley 2009), as well as avoidance of pain and obstacles (Higgins 1997). Convenience is a relative concept
concerned with the efficiency in time and effort as well as reduction in pain and solution to problems (Engdahl 2015). Convenience is an advantage in favor of a specific action to the detriment of alternative actions. White-collar offenders choose the most convenient path to reach their goals (Wikstrom, Mann, and Hardie 2018). Convenience is in line with the definition of a criminal opportunity, which is “the presence of a favorable combination of circumstances that renders a possible course of action” (Aguilera and Vadera 2008:434).
Historical research on gender and white-collar crime suggests that women were often victims of white-collar offenses, but seldom the offender (Robb 2006). Today research clearly shows that females do commit white-collar crime, but at a lower percentage than males (Daly 1989; Gottschalk and Glasø 2013; Gottschalk and Smith 2015). Researchers are starting to consider gender differences in white-collar crime (e.g., Benson and Gottschalk 2015; Benson
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and Harbinson, 2020; Galvin 2020; Gottschalk and Glasø 2013; Goulette 2020; Klenowski, Copes, and Mullins 2011; Ndrecka 2020; Reese and McDougal 2018). While gender and white-collar crime research in its infancy, there has been little consideration on differences in the nature of the white-collar offense between genders.
Numerous criminological theories suggest that offenders will pick a course of action that most easily benefits themselves. Convenience theory makes this notion most pronounced (Gottschalk 2017, 2020). Given traditional differences in roles and relationships between genders, it is likely that the choice of white-collar offenses is different depending on gender.
One unexplored area is target selection. If offenders are going to choose crime, which are most convenient, it is likely they will also choose targets, which are most convenient. We seek to examine these differences. As such, our research question is the following: How does gender affect target selection when committing white-collar crime?
It is widely known that victims and offenders often have some known relationship rather than being complete strangers (e.g., Chan, Myers, and Heide 2010; Ullman and Siegel 1993). Given this relationship, it is worth considering whether white-collar criminals have a similar pattern of victim-offender relationships. Second, we suggest that gender may have an impact on the type of victims the offender will choose. Given traditional gender norms around work and relationships, a stereotypical suggestion is that females are more likely to target friends or family. Another stereotypical suggestion is that males are more likely to target other known victims, such as employers or business partners. The argument for such suggestions is that females find it relatively more convenient in personal relationships, while males find it relatively more convenient in professional relationships.
According to feminist theory, women tend to be more fearful of committing crime because they feel more vulnerable towards the consequences of committing it (Britton 2000).
Thus, women will select targets where they expect less severe consequences. Furthermore,
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Bowman and Giligan (2008) suggest that women do not have the same capacity to justify criminal actions, which is an explanation founded in moral theory.
Materials and Methods
We obtained data from Utah’s White Collar Crime Offender Registry (Utah Attorney General’s Office 2020). The Utah Legislature authorized this online registry, HB 378, in 2015. Individuals in the state of Utah who are convicted of a second-degree white-collar crime felony or higher are required to register. The categories of crime included from statute HB 378 are securities fraud, theft by deception, unlawful dealing of property by fiduciary, fraudulent insurance, mortgage fraud, communications fraud, and money laundering (White Collar Crime Registry HB 378 2015). Generally, individuals are on the registry for 10 years for each offense unless they are convicted three or more times, in which case they are on the registry for life.
In order to create a database of white-collar offenders, we transcribed each offender from the registry into a dataset. Using the registry website, we were able to obtain data on 268 offenders and their crime. The demographic collected included: date of birth, height, weight, eye color, and hair color. Information about the offense included conviction type, conviction date, court location, target, and an offense summary. From the information provided on the registry, we were also able to include gender and age at when the offense(s) was committed.
We classify white-collar crime in several categories to identify target groups. For example, when a white-collar offender commits tax evasion, the target is society. When an offender commits bank fraud, then it is the bank and ultimately other bank customers who receive financial harm. When a white-collar offender commits embezzlement, then the employer is the victim. When an offender receives a bribe, then the briber’s competitors become potential victims.
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Assuming gender differences in crime, we might move back to the various targets and reason how males versus females select targets. For example, bank fraud might lead to serious consequences compared to embezzlement. While banks may take action, employers may dismiss a detected offender and not report it to the police, because the employer does not want public attention (Gottschalk and Tcherni-Buzzeo 2017). Then we might argue that female offenders prefer embezzlement more strongly as the result of women being more concerned about consequences.
Results
Overall, the dataset included 71 female offenders (26.5%), 191 male offenders (71.3%) and six unknown offenders (2.2%). We found that males are, on average, older than females when committing white-collar crime (t(260)=2.59, p=.01). Males averaged 43.5 years old compared to females average of 39.1 years old.
To understand better the convictions committed by each gender we included the first three white-collar crime convictions for the convicted, which led to entry on the white-collar crime registry. Conviction type is significantly different by gender (χ2 (7)=15.5, p=.03). Due to low cell count on some rows, we also ran a Fisher’s exact test, which was significant (p=.01). While not an exact test, we examined the by-cell Chi-square effects; the largest contributor to the overall chi-square value was securities fraud, which males committed in 95% of all convictions. Table 1 shows convictions by gender.
TABLE 1 NEAR HERE Target Selection
Our research question is concerned with the target selection by gender. In some instances, the offender had multiple targets for one conviction. We included up to three victims, which was consistent with the maximum number of victims per offense on the registry itself. There was a significant difference in the selection of targets between genders
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(χ2=75.4, p=<.001). When we examined Chi-square effect by cell, females were more likely to target their employers, healthcare providers, and random individuals while being less likely to target friends, acquaintances and investors. Table 2 shows target selection and gender.
TABLE 2 NEAR HERE Crime and Target Selection
Certain categories of white collar-crime necessitate a specific type of victim. For example, securities fraud is inherently going to involve investors as victims. At the same time, an offender could select specific groups, such as members of a specific religious affiliation as the target of victimized investors (i.e., affinity fraud). In order to improve our inquiry into the relationship between crime category, target selection and gender type, we specifically focused on target selection within a certain category of white-collar crime. Because there were a limited number of female offenders, we were only able to examine three categories of crime in this manner; that is communications fraud, theft by deception, and insurance fraud. Similar to the target selection section above, we included up to three victims per crime. In addition, to consider victim differences, we grouped victim categories in the following manner:
1. Known targets included; friends and acquaintances, and family members.
2. Workplace targets included; business collaborates, clients or customers, employers, employees, investors, retailers, and employees.
3. Organization targets included; businesses/business owners, insurance companies, financial institutions, government agencies, retailers.
4. Demographic targets included; the elderly, minors, single people in a dating
environment, members of a specific religious affiliation, home buyers/sellers/owners, healthcare providers, immigrants, buyers and sellers in classified ads, people with substance abuse problems, and people with physical health issues.
5. Random individuals were the remaining target category.
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Workplace targets comprised 32% (n=39) of communications fraud, followed closely by organizational targets at 31% (n=37). Significant differences between male and female targets were found (χ2=23.4(4), p=<.001). It appears females are more likely to select
workplace or random targets while being less likely to select organizational or known targets compared to males. Table 3 shows the differences in target selection for communications fraud, theft by deceptions, and insurance fraud by gender.
TABLE 3 NEAR HERE
Organizational victims were the target in 41% (n=31) of theft by deception convictions, with workplace victims being next most likely (37%, n=28). A moderately significant difference between male and female target selection was found (χ2=8.7(4), p=.07).
Likewise, Fisher’s exact test was moderately significant (p=.05). It appears that females were less likely to select organizational targets. Table 4 shows the gender differences between target selections for theft by deception convictions.
Insurance fraud was unique as this crime largely dictates a specific type of victim. Naturally, organizational victims in terms of insurance firms were the most likely targets for insurance fraud (86%, n=49). There were no significant differences between gender and target selection for insurance fraud (χ2=3.2(3), p=.36). Due to low cell counts, a Fisher’s exact test was also computed, but no significant difference was found (p=.41).
Discussion
We find support for gender differences in target selection as suggested by our research question concerned with how gender affects target choice in white-collar crime. Females were more likely to target their employers, health care providers, and random individuals. They were less likely to target friends, other acquaintances, and investors. Furthermore, it appears that females are more likely to select workplace or random targets while being less likely to select organizational or known targets compared to males.
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The selection of workplace targets by females also finds support in a study of white- collar offenders in Norway. Females were significantly more involved in embezzlement compared to males. On the other hand, they were far less involved in corruption. The latter result might just as much be a result of limited opportunities as the result of target selection (Benson and Gottschalk 2015; Gottschalk 2020; Gottschalk and Glasø 2013; Gottschalk and Smith 2015).
We find support for gender differences in white-collar crime choices. We find that certain categories of white-collar crime, such as insurance fraud, constrain the target selection.
Furthermore, we find that gender operates within the constrained context of target selection.
This is in line with the theory of convenience, when we exclude the perspective of opportunity differences for women versus men, which is mainly a matter of access to premises, resources, and systems in an organizational context to commit and conceal crime (Benson and Simpson 2018; Logan, Morgan, Benson, and Cullen 2019). Other elements in the opportunity structure, such as decay by disorganized institutional deterioration
(Rodriguez, Uhlenbruck, and Eden 2005), chaos by lack of oversight and guardianship (Bosse and Phillips 2016), and collapse by criminal market structures (Goncharov and Peter 2019), are usually the same for female and male offenders (Gottschalk 2017, 2020).
Dodge (2007) found that women tend to talk most strongly about ethics, morals, and social responsibility. It seems almost impossible for others to think at the same time that they are criminals. However, research by O’Fallon and Butterfield (2005) indicates that there is no difference between women and men when it comes to making ethical and unethical decisions.
In our perspective of convenience, lack of suspicion leads to lack of crime signal detection (Szalma and Hancock 2013). A typical employer will seldom suspect female employees of misconduct and crime as compared to male suspects.
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A simple experiment we have often performed in different audiences is the question who you would bribe. You would like to build a new home on a property that the state has regulated for recreation, such as an attractive shoreline. You have the choice of bribing a female or male official in the municipality. Considering all the audiences, a large majority vote almost exclusively men. Almost no one would bribe a female official.
Victimization of potential targets in white-collar crime is not just a matter of relative detection risk for males versus females. Justification of crime is also important in relation to the motive, the situation, and the target. An interesting avenue for gender study might be to interview both female and male offenders in terms of their application of neutralization techniques. We do expect to find gender differences in denial of responsibility, denial of injury, denial of victim, condemnation of the condemners, and appeal to higher loyalties (Sykes and Matza 1957) when it comes to alternative targets for white-collar crime.
Conclusion
We find support for gender differences in target selection as suggested by our research question concerned with how gender affects target choice in white-collar crime. Females were more likely to target their employers, health care providers, and random individuals. They were less likely to target friends, other acquaintances, and investors. Furthermore, it appears that females are more likely to select workplace or random targets while being less likely to select organizational or known targets compared to males. Based on the theory of
convenience, we argued that male and female offenders vary in their perceptions of
convenience when considering alternative categories of crime and alternative categories of victims. We obtained data from Utah’s White Collar Crime Offender Registry. Individuals in the state of Utah who are convicted of a second-degree white-collar crime felony or higher are required to register.
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Victimization of potential targets in white-collar crime is not just a matter of relative detection risk for males versus females. Justification of crime is also important in relation to the motive, the situation, and the target. An interesting avenue for gender study might be to interview both female and male offenders in terms of their application of neutralization techniques. We do expect to find gender differences in denial of responsibility, denial of injury, denial of victim, condemnation of the condemners, and appeal to higher loyalties (Sykes and Matza 1957) when it comes to alternative targets for white-collar crime.
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17 Table 1: Conviction by Gender
Conviction
Gender
Male Female Total Communications Fraud 69 (30%) 30 (36%) 99 (31.5%)
Insurance Fraud 27 (11.7%) 13 (15.5%) 40 (12.7%)
Money Laundering 14 (6.0%) 4 (4.8%) 18 (5.7%)
Mortgage Fraud 0 (0%) 1 (1.2%) 1 (0.3%)
Pattern of Unlawful Activity 30 (13.0%) 9 (10.7%) 39 (12.4%)
Securities Fraud 38 (16.5%) 2 (2.4%) 40 (12.7%)
Theft by Deception 43 (18.7%) 20 (23.8%) 64 (20.1%) Unlawful Dealing with Property
by a Fiduciary
9 (3.9%) 5 (6.0%) 14 (4.5%)
Total 230 84 314
* Percentages are within gender (i.e., column total)
18 Table 2: Target Selection by Gender
Conviction
Gender
Male Female Total
Business Partners 6 (2.6%) 0 (0%) 6 (1.9%)
Buyers/Sellers via Classified Ads
4 (1.7%) 0 (0%) 4 (1.3%)
Clients or Customers 12 (5.2%) 2 (5.3%) 14 (4.5%)
Elderly 6 (2.6%) 0 (0%) 6 (1.9%)
Employees 1 (0.4%) 1 (2.6%) 2 (0.6%)
Employer 16 (7.0%) 29 (76.3%) 45 (14.4%)
Family Members 15 (6.5%) 4 (10.5%) 19 (6.1%)
Financial Institutions 15 (6.5%) 2 (5.3%) 17 (5.4%) Friends & Acquaintances 33 (14.3%) 5 (13.2%) 38 12.1%)
Government Agencies 6 (2.6%) 1 (2.6%) 7 (2.2%)
Healthcare Providers 0 (0%) 3 (7.9%) 3 (1.0%)
Home Buyers/Sellers/Owners 11 (4.8%) 2 (5.3%) 13 (4.2%) Immigrants and Non-English
Speaking Populations
1 (0.4%) 1 (2.6%) 2 (0.6%) Insurance Companies 26 (11.3%) 13 (34.2%) 39 (12.5%)
Investors 28 (12.2%) 1 (2.6%) 29 (9.3%)
Members of a Specific Religious Group
3 (1.3%) 1 (2.6%) 4 (1.3%)
Minors 1 (0.4%) 0 (0%) 1 (0.3%)
People with Physical Health Issues and Disabilities
1 (0.4%) 1 (2.6%) 2 (0.6%) People with Substance Abuse
Problems
9 (3.9%) 2 (5.3%) 11 (3.5%) Random Individual(s) 3 (1.3%) 6 (15.8%) 9 (2.9%)
Retailers 20 (8.7%) 5 (13.2%) 25 (8.0%)
Single People in a Dating Environment
2 (0.9%) 0 (0%) 2 (0.6%)
Small Businesses/ Small Business Owners
11 (4.8%) 4 (10.5%) 15 (4.8%)
Total 230 83 313
* Percent are within gender (i.e., column total)
19 Table 3: Target Selection by Gender and Crime
* Percent are within gender (i.e., column total)
Communications Fraud Theft by Deception Insurance Fraud
Target Male Female Total Male Female Total Male Female Total
Demographic
16 (18.6%)
3 (9.4%)
19 (16.1%)
5 (10.2%)
1 (3.8%)
6 (8.0%)
0 (0%)
1 (7.1%)
1 (2.1%)
Known
19 (22.1%)
2 (6.3%)
21 (17.8%)
3 (6.1%)
4 (15.4%)
7 (9.3%)
2 (6.1%)
0 (0%)
2 (4.3%)
Organization
31 (36%)
5 (15.6%)
36 (30.5%)
25 (51.0%)
6 (23.1%)
31 (41.3%)
28 (84.8%)
12 (85.7%)
40 (85.1%)
Workplace
19 (22.1 %) 18 (56.3%) 37 (31.4%) 15 (30.6%) 13 (50.0%) 28 (37.3%) 3 (9.1%) 1 (7.1%) 4 (8.5%)
Random
1 (1.2%)
4 (12.5%)
5 (4.2%)
1 (2.0%)
2 (7.7%)
3 (4.0%)
0 (0%)
0 (0%)
0 (0%)
Total 86 32 118 49 26 75 33 14 47
